






import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__a:     Dict   					=					logging.getLogger(__name__)


__a:     Union[str, Any]   					=					list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__a:     Optional[int]   					=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={
          '''help''': (
              '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
              ''' scratch.'''
          )
      }					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__lowerCAmelCase     )}					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,      )



@dataclass
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''The input training data file (a text file).'''}     )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={
          '''help''': (
              '''The input training data files (multiple files in glob format). '''
              '''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
          )
      }					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}					,      )

  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''}     )
  _lowerCamelCase    =  field(default=__lowerCAmelCase					,      metadata={'''help''': '''Whether ot not to use whole word mask.'''}     )
  _lowerCamelCase    =  field(
      default=0.15					,      metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}     )
  _lowerCamelCase    =  field(
      default=1 / 6					,      metadata={
          '''help''': (
              '''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
              ''' modeling.'''
          )
      }					,      )
  _lowerCamelCase    =  field(
      default=5					,      metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''}     )

  _lowerCamelCase    =  field(
      default=-1					,      metadata={
          '''help''': (
              '''Optional input sequence length after tokenization.'''
              '''The training dataset will be truncated in block of this size for training.'''
              '''Default to the model max input length for single sentence inputs (take into account special tokens).'''
          )
      }					,      )
  _lowerCamelCase    =  field(
      default=__lowerCAmelCase					,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case = False   ,      __snake_case = None   ,      )				->						List[Any]:
       def _dataset(__snake_case   ,      __snake_case=None ):
              if args.line_by_line:
                     if ref_path is not None:
                            if not args.whole_word_mask or not args.mlm:
                                   raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
                            return LineByLineWithRefDataset(
                                tokenizer=UpperCamelCase_   ,      file_path=UpperCamelCase_   ,      block_size=args.block_size   ,      ref_path=UpperCamelCase_   ,      )

                     return LineByLineTextDataset(tokenizer=UpperCamelCase_   ,      file_path=UpperCamelCase_   ,      block_size=args.block_size )
              else:
                     return TextDataset(
                         tokenizer=UpperCamelCase_   ,      file_path=UpperCamelCase_   ,      block_size=args.block_size   ,      overwrite_cache=args.overwrite_cache   ,      cache_dir=UpperCamelCase_   ,      )

       if evaluate:
              return _dataset(args.eval_data_file   ,      args.eval_ref_file )
       elif args.train_data_files:
              return ConcatDataset([_dataset(UpperCamelCase_ ) for f in glob(args.train_data_files )] )
       else:
              return _dataset(args.train_data_file   ,      args.train_ref_file )




def       _SCREAMING_SNAKE_CASE (							)				->						Dict:
       _UpperCAmelCase							=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    parser.parse_args_into_dataclasses()

       if data_args.eval_data_file is None and training_args.do_eval:
              raise ValueError(
                  """Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
                  """or remove the --do_eval argument.""" )
       if (
           os.path.exists(training_args.output_dir )
           and os.listdir(training_args.output_dir )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  """ --overwrite_output_dir to overcome.""" )

       # Setup logging
       logging.basicConfig(
           format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,      datefmt="""%m/%d/%Y %H:%M:%S"""   ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,      )
       logger.warning(
           """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""   ,      training_args.local_rank   ,      training_args.device   ,      training_args.n_gpu   ,      bool(training_args.local_rank != -1 )   ,      training_args.fpaa   ,      )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info("""Training/evaluation parameters %s"""   ,      UpperCamelCase_ )

       # Set seed
       set_seed(training_args.seed )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       if model_args.config_name:
              _UpperCAmelCase							=    AutoConfig.from_pretrained(model_args.config_name   ,      cache_dir=model_args.cache_dir )
       elif model_args.model_name_or_path:
              _UpperCAmelCase							=    AutoConfig.from_pretrained(model_args.model_name_or_path   ,      cache_dir=model_args.cache_dir )
       else:
              _UpperCAmelCase							=    CONFIG_MAPPING[model_args.model_type]()
              logger.warning("""You are instantiating a new config instance from scratch.""" )

       if model_args.tokenizer_name:
              _UpperCAmelCase							=    AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,      cache_dir=model_args.cache_dir )
       elif model_args.model_name_or_path:
              _UpperCAmelCase							=    AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,      cache_dir=model_args.cache_dir )
       else:
              raise ValueError(
                  """You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
                  """ script, save it,and load it from here, using --tokenizer_name""" )

       if model_args.model_name_or_path:
              _UpperCAmelCase							=    AutoModelWithLMHead.from_pretrained(
                  model_args.model_name_or_path   ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )   ,      config=UpperCamelCase_   ,      cache_dir=model_args.cache_dir   ,      )
       else:
              logger.info("""Training new model from scratch""" )
              _UpperCAmelCase							=    AutoModelWithLMHead.from_config(UpperCamelCase_ )

       model.resize_token_embeddings(len(UpperCamelCase_ ) )

       if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
              raise ValueError(
                  """BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
                  """--mlm flag (masked language modeling).""" )

       if data_args.block_size <= 0:
              _UpperCAmelCase							=    tokenizer.max_len
              # Our input block size will be the max possible for the model
       else:
              _UpperCAmelCase							=    min(data_args.block_size   ,      tokenizer.max_len )

       # Get datasets

       _UpperCAmelCase							=    (
           get_dataset(UpperCamelCase_   ,      tokenizer=UpperCamelCase_   ,      cache_dir=model_args.cache_dir ) if training_args.do_train else None
       )
       _UpperCAmelCase							=    (
           get_dataset(UpperCamelCase_   ,      tokenizer=UpperCamelCase_   ,      evaluate=UpperCamelCase_   ,      cache_dir=model_args.cache_dir )
           if training_args.do_eval
           else None
       )
       if config.model_type == "xlnet":
              _UpperCAmelCase							=    DataCollatorForPermutationLanguageModeling(
                  tokenizer=UpperCamelCase_   ,      plm_probability=data_args.plm_probability   ,      max_span_length=data_args.max_span_length   ,      )
       else:
              if data_args.mlm and data_args.whole_word_mask:
                     _UpperCAmelCase							=    DataCollatorForWholeWordMask(
                         tokenizer=UpperCamelCase_   ,      mlm_probability=data_args.mlm_probability )
              else:
                     _UpperCAmelCase							=    DataCollatorForLanguageModeling(
                         tokenizer=UpperCamelCase_   ,      mlm=data_args.mlm   ,      mlm_probability=data_args.mlm_probability )

                      # Initialize our Trainer
       _UpperCAmelCase							=    Trainer(
           model=UpperCamelCase_   ,      args=UpperCamelCase_   ,      data_collator=UpperCamelCase_   ,      train_dataset=UpperCamelCase_   ,      eval_dataset=UpperCamelCase_   ,      prediction_loss_only=UpperCamelCase_   ,      )

       # Training
       if training_args.do_train:
              _UpperCAmelCase							=    (
                  model_args.model_name_or_path
                  if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
                  else None
              )
              trainer.train(model_path=UpperCamelCase_ )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir )

                      # Evaluation
       _UpperCAmelCase							=    {}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***""" )

              _UpperCAmelCase							=    trainer.evaluate()

              _UpperCAmelCase							=    math.exp(eval_output["""eval_loss"""] )
              _UpperCAmelCase							=    {"""perplexity""": perplexity}

              _UpperCAmelCase							=    os.path.join(training_args.output_dir   ,      """eval_results_lm.txt""" )
              if trainer.is_world_master():
                     with open(UpperCamelCase_   ,      """w""" ) as writer:
                            logger.info("""***** Eval results *****""" )
                            for key in sorted(result.keys() ):
                                   logger.info("""  %s = %s"""   ,      UpperCamelCase_   ,      str(result[key] ) )
                                   writer.write("""%s = %s\n""" % (key, str(result[key] )) )

              results.update(UpperCamelCase_ )

       return results




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Tuple:
       main()


if __name__ == "__main__":
  main()
import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _SCREAMING_SNAKE_CASE 		=      """0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if rng is None:
  UpperCamelCase			  =      random.Random()

 UpperCamelCase			  =      1
 for dim in shape:
  total_dims *= dim

 UpperCamelCase			  =      []
 for _ in range(UpperCamelCase_       ):
  values.append(rng.randint(0							,					vocab_size - 1       )       )

 UpperCamelCase			  =      np.array(UpperCamelCase_							,					dtype=jnp.intaa       ).reshape(UpperCamelCase_       )

 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ids_tensor(UpperCamelCase_							,					vocab_size=2							,					rng=UpperCamelCase_       )
 # make sure that at least one token is attended to for each batch
 UpperCamelCase			  =      1
 return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    # cut to half length & take max batch_size 3
    UpperCamelCase			  =      2
    UpperCamelCase			  =      inputs["""input_ids"""].shape[-1] // 2
    UpperCamelCase			  =      inputs["""input_ids"""][:max_batch_size, :sequence_length]

    UpperCamelCase			  =      jnp.ones_like(lowerCamelCase_				)
    UpperCamelCase			  =      attention_mask[:max_batch_size, :sequence_length]

    # generate max 5 tokens
    UpperCamelCase			  =      input_ids.shape[-1] + 5
    if config.eos_token_id is not None and config.pad_token_id is None:
     # hack to allow generate for models such as GPT2 as is done in `generate()`
     UpperCamelCase			  =      config.eos_token_id
    return config, input_ids, attention_mask, max_length







   @is_pt_flax_cross_test
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
     UpperCamelCase			  =      getattr(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      pt_model_class(lowerCamelCase_				).eval()
     UpperCamelCase			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,   flax_model.params				)

     UpperCamelCase			  =      flax_model.generate(lowerCamelCase_				).sequences
     UpperCamelCase			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,   dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      UpperCamelCase			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

     self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,   flax_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[0]   ,   input_ids.shape[0] * config.num_return_sequences				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0.8
    UpperCamelCase			  =      10
    UpperCamelCase			  =      0.3
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      2
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
    UpperCamelCase			  =      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

    UpperCamelCase			  =      """Hello world"""
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   return_tensors="""np"""				).input_ids

    # typos are quickly detected (the correct argument is `do_sample`)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """do_samples"""				):
     model.generate(lowerCamelCase_   ,   do_samples=lowerCamelCase_				)

    # arbitrary arguments that will not be used anywhere are also not accepted
    with self.assertRaisesRegex(lowerCamelCase_   ,   """foo"""				):
     UpperCamelCase			  =      {"""foo""": """bar"""}
     model.generate(lowerCamelCase_   ,   **lowerCamelCase_				)
